In recent years, network analyses of brain imaging data have increased in popularity. Such analyses allow investigators to describe the structural or functional organization of the brain as a whole, rather than focusing only on the areas with the strongest signal. Network analyses allow characterization of a network as a whole, as well as how each network node contributes to the network. Furthermore, hierarchical organization of brain networks can be described as a collection of tightly interconnected clusters of nodes, known as modules. Such modules often spatially coincide with brain areas relevant to certain cognitive and sensory processes. Compared to other types of network data, brain network data have unique properties. The first property is that multiple realizations of the brain network can be observed from multiple subjects, which is simply impossible in many social or technological networks since there is only one network of interest. Secondly, brain networks can be aligned across subjects at each node (which can be an anatomical area or a voxel) or at functionally relevant modules. One way to take advantage of these properties is to combine brain network data. This enables researchers to investigate consistent network properties across subjects. In this proposal, a methodological framework will be developed to analyze multiple network data together. At first, a framework for a group analysis will be developed by building a group brain network from multiple subjects. This can be accomplished by aligning networks across subjects at each node (Specific Aim 1) or module (Specific Aim 2). In addition to combining networks across subjects, a framework to combine brain network data across image modalities will be developed in order to examine similarities and differences in structural and functional brain networks (Specific Aim 3). Multiple structural and functional brain network studies have indicated commonalities in the network structures and the location of key nodes. Thus, the proposed methods will provide a highly needed tool for investigations of such structure-function relationships.